Results 141 to 150 of about 15,041 (261)

Enabling Sparse CCA for Mix Source Separation in High-Dimensional Data

IEEE Access
Sparse canonical correlation analysis is a powerful method for capturing the relationships between two multidimensional datasets and can be extended for mix source separation (MSS) by leveraging the autocorrelation matrix.
Muhammad Usman Khalid
doaj   +1 more source

Hyper-graph based sparse canonical correlation analysis for the diagnosis of Alzheimer's disease from multi-dimensional genomic data. [PDF]

Methods, 2021
Shao W, Xiang S, Zhang Z, Huang K, Zhang J.   +4 more
europepmc   +1 more source

Canonical correlation methods for exploring microbe-environment interactions in deep subsurface

, 2015
In this study, we apply non-linear kernelized canonical correlation analysis (KCCA) as well as primal-dual sparse canonical correlation analysis (SCCA) to the discovery of correlations between sulphate reducing bacterial taxa and their geochemical ...
Bomberg, Malin, Nybo, Kristian, Malin Bomberg, Uurtio, Viivi, Itävaara, Merja, Kristian Nybo, Viivi Uurtio, Merja Itävaara, Rousu, Juho, Juho Rousu   +9 more
core   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

A Bayesian Methodology for Estimation for Sparse Canonical Correlation

, 2023
It can be challenging to perform an integrative statistical analysis of multi-view high-dimensional data acquired from different experiments on each subject who participated in a joint study.
Gaskins, Jeremy T., Kulkarni, Siddhesh, Pal, Subhadip   +2 more
core  

Artificial Intelligence‐Driven Network Pharmacology: A Methodological Paradigm Shift Bridging Traditional Wisdom and Modern Science

Advanced Intelligent Discovery, EarlyView.
Artificial intelligence is redefining network pharmacology (NP). By integrating knowledge graph engineering, geometric deep learning, multiomics anchoring, and generative reasoning, AI‐driven NP (AI‐NP) transforms static target mapping into dynamic, predictive modeling.
Cong Wang, Sile Hu, Rigahai Ji, Riguna A, Siqige Hao, Ma Zhuo, Yi‐Hung Chen, Bisihalatu Hu, Ulana Bai, Xigui Hada   +9 more
wiley   +1 more source

Retinal Vessel Segmentation: A Comprehensive Review From Classical Methods to Deep Learning Advances (1982–2025)

Advanced Intelligent Systems, EarlyView.
Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal, Jan Kubíček, Jana Bahrová, Alice Varyšová, Martin Augustýnek, Marek Penhaker, Juraj Timkovič   +6 more
wiley   +1 more source

Smccnet 2.0: a comprehensive tool for multi-omics network inference with shiny visualization

BMC Bioinformatics
Summary Sparse multiple canonical correlation network analysis (SmCCNet) is a machine learning technique for integrating omics data along with a variable of interest (e.g., phenotype of complex disease), and reconstructing multi-omics networks that are ...
Weixuan Liu, Thao Vu, Iain R. Konigsberg, Katherine A. Pratte, Yonghua Zhuang, Katerina J. Kechris   +5 more
doaj   +1 more source

DiffRS-net: A Novel Framework for Classifying Breast Cancer Subtypes on Multi-Omics Data

Applied Sciences
The precise classification of breast cancer subtypes is crucial for clinical diagnosis and treatment, yet early symptoms are often subtle. The use of multi-omics data from high-throughput sequencing can improve the classification accuracy.
Pingfan Zeng, Cuiyu Huang, Yiran Huang
doaj   +1 more source